UHR-BAT2

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Hugging Face · ModelScope · Evaluation details

A Qwen3-VL-8B model fine-tuned for remote-sensing visual question answering.

UHR-BAT2 is a full-weight fine-tune of Qwen3-VL-8B-Instruct, trained on a selected 10,000-example GeoLLaVA manifest. It keeps the native Qwen3-VL architecture and can be loaded with Transformers. The released checkpoint is qwen3vl-8b-geollava-ft3-selected-10k.

Main results

These are complete, unpruned model evaluations, audited against existing prediction records on 2026-10-13. The reported results use the corrected option-scoring protocol.

Dataset	Accuracy	Correct / Total	Metric
RSHR-Bench	50.72%	980 / 1,932	All-QA option accuracy
XLRS-Bench-lite	52.27%	1,610 / 3,080	Strict option-set accuracy
MME-RealWorld Remote Sensing	56.80%	2,123 / 3,738	Full remote-sensing subset accuracy

RSHR's single-turn subset is 45.66% (726/1,590). XLRS includes 52.45% (1,563/2,980) on single-choice questions and 47.00% (47/100) on multi-select tasks. MME uses the full 3,738-record remote-sensing subset, not its 150-record Lite subset.

The evaluated image budget is min_pixels=784, max_pixels=1,310,720 per image. Images are resized within this budget. Single-choice evaluation uses FP32 candidate-option logits; XLRS multi-select evaluation uses constrained decoding and exact option-set matching. Ordinary free generation is not the same scoring procedure. See EVALUATION.md and evaluation_results.json for categories, denominators and protocol details.

Model and training

Item	Value
Base model	Qwen/Qwen3-VL-8B-Instruct
Architecture	Qwen3VLForConditionalGeneration
Training data	GeoLLaVA, selected 10K examples
Fine-tuning	Frozen vision encoder; train merger and LLM
Checkpoint format	Full BF16 safetensors, 4 shards
Training completion	1 epoch, 625 optimizer steps
Launch configuration	Adafactor; learning rate 1e-5; global batch 16; maximum sequence length 8,192
Release scope	Dense checkpoint and inference example

The checkpoint's trainer state confirms one completed epoch and 625 steps. Other training settings above are recorded launcher defaults. This is the final training checkpoint; no validation-selected best checkpoint was recorded. The original UHR-BAT uses a different architecture; its custom compression modules and results are not part of this Qwen3-VL weight release.

Quick start

Use Python 3.10 or later and a CUDA-enabled PyTorch installation. A single image can be processed with the standard Qwen3-VL implementation; custom remote code is not required. GPU memory requirements depend on image size and output length.

pip install -r requirements.txt
python inference.py --model RL-MIND/UHR-BAT2 \
  --image /path/to/remote_sensing_image.jpg \
  --question "Describe the land use and visible objects in this image."

import torch
from PIL import Image
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration

model_id = "RL-MIND/UHR-BAT2"
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3VLForConditionalGeneration.from_pretrained(
    model_id, dtype="auto", device_map="auto", attn_implementation="sdpa"
).eval()

image = Image.open("remote_sensing_image.jpg").convert("RGB")
messages = [{"role": "user", "content": [
    {"type": "image"},
    {"type": "text", "text": "Describe this remote-sensing image."},
]}]
prompt = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = processor(text=[prompt], images=[image], return_tensors="pt")
inputs.pop("token_type_ids", None)
inputs = inputs.to(model.device)
with torch.inference_mode():
    output = model.generate(
        **inputs, max_new_tokens=128, do_sample=False,
        temperature=None, top_p=None, top_k=None, use_cache=True,
    )
answer = output[:, inputs["input_ids"].shape[1]:]
print(processor.batch_decode(answer, skip_special_tokens=True)[0])

For ModelScope, download the same package and pass the local directory to the example above:

pip install modelscope

from modelscope import snapshot_download
model_id = snapshot_download("KAIWANG/UHR-BAT2")
# Use model_id with AutoProcessor and Qwen3VLForConditionalGeneration above.

The example demonstrates image-conditioned generation. It does not reproduce the option-scoring evaluation table by itself.

The release passed a native loading and short image-generation smoke test with Python 3.11.0, PyTorch 2.5.1+cu121, Transformers 4.57.0.dev0 and Accelerate 1.13.0. The test checks package loading and generation, not benchmark accuracy or inference speed. File hashes and tensor metadata are in release_manifest.json.

Limitations

Counting and small-object localization remain challenging. These resized-input evaluations do not establish native full-resolution ultra-high-resolution performance. A matched evaluation of the unfine-tuned base model is not included, so this release makes no controlled fine-tuning improvement claim. A training-data audit found candidate scene-name/tile matches with RSHR; pixel-level and question-level overlap have not been resolved, so geographic-disjoint generalization is not established.

License and acknowledgments

Released under Apache-2.0, following the base model's license. See NOTICE for the upstream attribution and modifications. Training and benchmark datasets are not redistributed; their own terms apply. We acknowledge the Qwen team, GeoLLaVA contributors and the benchmark authors.